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# Deep Medical Diagnosis

QA solving using DL methods applied to medical diagnosis.

## Dataset

We have a dataset of 2350 diseases with their symptoms. The data format is similar to the dataset from 
babi task (Facebook). For example, for the disease Achasia:

![Achasia](http://www.diegoacuna.me/factlist.png?raw=true)

### Exploratory analysis of the dataset

We have 2350 diseases on the dataset. Augmentation has been applied to the original dataset by replacing words by their synonyms and deleting and permuting
facts randomly. The final dataset consists of:

* Vocabulary size: 21520
* Story max length: 1647
* Number of training stories: 133093
* Number of test stories: 59394


Here's how a sample looks like (input, disease):

([[u'adult', u'symptoms', u'of', u'likewise', u'begin', u'to', u'be', u'far', u'more', u'subtle', u'than', u'childhood', u'symptoms', u'.'], [u'being', u'unable', u'to', u'stick', u'at', u'tasks', u'that', u'are', u'borings', u'or', u'clip', u'down', u'.'], [u'but', u'it', u's', u'recognise', u'that', u'symptoms', u'of', u'prevail', u'from', u'childhood', u'into', u'a', u'person', u's', u'teenage', u'and', u'then', u'adulthood', u'.'], [u'carelessnesses', u'and', u'want', u'of', u'to', u'detail', u'.'], [u'continually', u'starting', u'new', u'tasks', u'before', u'finish', u'old', u'ones', u'.'], [u'being', u'unable', u'to', u'postponement', u'their', u'bend', u'.'], [u'some', u'specialists', u'have', u'suggest', u'the', u'followers', u'list', u'of', u'symptoms', u'associated', u'with', u'in', u'adults', u'.'], [u'the', u'symptoms', u'of', u'can', u'be', u'categorize', u'into', u'two', u'types', u'of', u'behavioral', u'jobs', u'.'], [u'interrupt', u'conversations', u'.'], [u'constantly', u'fidget', u'.'], [u'having', u'difficulty', u'coordinate', u'tasks', u'.'], [u'the', u'main', u'signs', u'of', u'and', u'impulsivenesses', u'are', u'.']],[u'attention deficit hyperactivity disorder'])

The next plots show 1) the number of facts in each sample and 2) the number of words in each fact:



 Facts x sample                   | Words x fact
:-------------------------:|:-------------------------:
<img src="https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/facts_by_disease.png?raw=true" width="350">  | <img src="https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/word_by_fact.png?raw=true" width="350" >

## Training a RNN 

A GRU network is trained on the dataset. The GRU consists of two layers of 128 units with a dropout of 0.5. The output is a softmax layer. Pre-trained word vectors are used as embedding layer. 
The network works very well on the training and test dataset but is suboptimal on self-made data. There are various possible ways to solve that which must be studied.

Accuracy curves on the validation set are shown in the next image

![Accuracy](https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/accuracy.png?raw=true)

An example of output for data from the dataset is shown next. It is interesting to notice that the network outputs related diseases as the most probable 5, meaning that is understanding that those diseases are related (in this case all are psychological disorders).

```
Predictions for data:
[[u'is', u'easily', u'distract', u'.'], [u'is', u'oftentimes', u'short', u'in', u'daily', u'activities', u'.'], [u'hyperactivity', u'symptoms', u'.'], [u'fidgetinesses', u'with', u'custodies', u'or', u'pess', u'or', u'wriggles', u'in', u'place', u'.'], [u'leaves', u'place', u'when', u'remain', u'sit', u'is', u'expect', u'.'], [u'runs', u'about', u'or', u'rises', u'in', u'inappropriate', u'situations', u'.'], [u'has', u'jobs', u'playing', u'or', u'workings', u'softly', u'.'], [u'is', u'oft', u'on', u'the', u'turn', u'acts', u'as', u'if', u'drive', u'by', u'a', u'motor', u'.'], [u'negotiations', u'excessively', u'.'], [u'impulsivity', u'symptoms', u'.'], [u'blurts', u'out', u'replies', u'before', u'enquiries', u'have', u'been', u'complete', u'.'], [u'has', u'trouble', u'look', u'crook', u'.'], [u'interrupts', u'or', u'irrupts', u'on', u'others', u'butts', u'into', u'conversations', u'or', u'games', u'.']]
Disease: attention deficit hyperactivity disorder
5 most prob. diseases: [u'attention deficit hyperactivity disorder', u'oppositional defiant disorder', u'seasonal affective disorder', u'anorexia nervosa', u'language disorder children']
```

Next, we test the neural network on self-made symptoms, some examples are shown next. While in the first case the network correctly identify the disease, in the second case does not.

```
[u'oily skin', u'painful touch skin', u'face affected almost everywhere', u'chest affected', u'some blackheads', u'a lot of papules', u'papules', u'nodules', u'cysts']
Disease: acne
5 most prob. diseases: [u'acne', u'genital warts', u'scarlet fever', u'erythema multiforme', u'measles']

[u'pulsating feeling in stomach', u'persistent back pain', u'abdominal pain', u'severe pain in the middle abdomen', u'dizziness', u'clammy skin', u'tachycardia', u'loss of consciousness']
Disease: abdominal aortic aneurysm
5 most prob. diseases: [u'pulmonary actinomycosis', u'mucormycosis', u'sleeping sickness', u'acute myeloid leukemia', u'bile duct obstruction']
```



	a/README.md		b/README.md
1	# Deep Medical Diagnosis	1	# Deep Medical Diagnosis
2		2
3	QA solving using DL methods applied to medical diagnosis.	3	QA solving using DL methods applied to medical diagnosis.
4		4
5	## Dataset	5	## Dataset
6		6
7	We have a dataset of 2350 diseases with their symptoms. The data format is similar to the dataset from	7	We have a dataset of 2350 diseases with their symptoms. The data format is similar to the dataset from
8	babi task (Facebook). For example, for the disease Achasia:	8	babi task (Facebook). For example, for the disease Achasia:
9		9
10	![Achasia](http://www.diegoacuna.me/factlist.png)	10	![Achasia](http://www.diegoacuna.me/factlist.png?raw=true)
11		11
12	### Exploratory analysis of the dataset	12	### Exploratory analysis of the dataset
13		13
14	We have 2350 diseases on the dataset. Augmentation has been applied to the original dataset by replacing words by their synonyms and deleting and permuting	14	We have 2350 diseases on the dataset. Augmentation has been applied to the original dataset by replacing words by their synonyms and deleting and permuting
15	facts randomly. The final dataset consists of:	15	facts randomly. The final dataset consists of:
16		16
17	* Vocabulary size: 21520	17	* Vocabulary size: 21520
18	* Story max length: 1647	18	* Story max length: 1647
19	* Number of training stories: 133093	19	* Number of training stories: 133093
20	* Number of test stories: 59394	20	* Number of test stories: 59394
21		21
22		22
23	Here's how a sample looks like (input, disease):	23	Here's how a sample looks like (input, disease):
24		24
25	([[u'adult', u'symptoms', u'of', u'likewise', u'begin', u'to', u'be', u'far', u'more', u'subtle', u'than', u'childhood', u'symptoms', u'.'], [u'being', u'unable', u'to', u'stick', u'at', u'tasks', u'that', u'are', u'borings', u'or', u'clip', u'down', u'.'], [u'but', u'it', u's', u'recognise', u'that', u'symptoms', u'of', u'prevail', u'from', u'childhood', u'into', u'a', u'person', u's', u'teenage', u'and', u'then', u'adulthood', u'.'], [u'carelessnesses', u'and', u'want', u'of', u'to', u'detail', u'.'], [u'continually', u'starting', u'new', u'tasks', u'before', u'finish', u'old', u'ones', u'.'], [u'being', u'unable', u'to', u'postponement', u'their', u'bend', u'.'], [u'some', u'specialists', u'have', u'suggest', u'the', u'followers', u'list', u'of', u'symptoms', u'associated', u'with', u'in', u'adults', u'.'], [u'the', u'symptoms', u'of', u'can', u'be', u'categorize', u'into', u'two', u'types', u'of', u'behavioral', u'jobs', u'.'], [u'interrupt', u'conversations', u'.'], [u'constantly', u'fidget', u'.'], [u'having', u'difficulty', u'coordinate', u'tasks', u'.'], [u'the', u'main', u'signs', u'of', u'and', u'impulsivenesses', u'are', u'.']],[u'attention deficit hyperactivity disorder'])	25	([[u'adult', u'symptoms', u'of', u'likewise', u'begin', u'to', u'be', u'far', u'more', u'subtle', u'than', u'childhood', u'symptoms', u'.'], [u'being', u'unable', u'to', u'stick', u'at', u'tasks', u'that', u'are', u'borings', u'or', u'clip', u'down', u'.'], [u'but', u'it', u's', u'recognise', u'that', u'symptoms', u'of', u'prevail', u'from', u'childhood', u'into', u'a', u'person', u's', u'teenage', u'and', u'then', u'adulthood', u'.'], [u'carelessnesses', u'and', u'want', u'of', u'to', u'detail', u'.'], [u'continually', u'starting', u'new', u'tasks', u'before', u'finish', u'old', u'ones', u'.'], [u'being', u'unable', u'to', u'postponement', u'their', u'bend', u'.'], [u'some', u'specialists', u'have', u'suggest', u'the', u'followers', u'list', u'of', u'symptoms', u'associated', u'with', u'in', u'adults', u'.'], [u'the', u'symptoms', u'of', u'can', u'be', u'categorize', u'into', u'two', u'types', u'of', u'behavioral', u'jobs', u'.'], [u'interrupt', u'conversations', u'.'], [u'constantly', u'fidget', u'.'], [u'having', u'difficulty', u'coordinate', u'tasks', u'.'], [u'the', u'main', u'signs', u'of', u'and', u'impulsivenesses', u'are', u'.']],[u'attention deficit hyperactivity disorder'])
26		26
27	The next plots show 1) the number of facts in each sample and 2) the number of words in each fact:	27	The next plots show 1) the number of facts in each sample and 2) the number of words in each fact:
28		28
29		29
30		30
31	Facts x sample \| Words x fact	31	Facts x sample \| Words x fact
32	:-------------------------:\|:-------------------------:	32	:-------------------------:\|:-------------------------:
33	<img src="https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/facts_by_disease.png" width="350"> \| <img src="https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/word_by_fact.png" width="350" >	33	<img src="https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/facts_by_disease.png?raw=true" width="350"> \| <img src="https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/word_by_fact.png?raw=true" width="350" >
34		34
35	## Training a RNN	35	## Training a RNN
36		36
37	A GRU network is trained on the dataset. The GRU consists of two layers of 128 units with a dropout of 0.5. The output is a softmax layer. Pre-trained word vectors are used as embedding layer.	37	A GRU network is trained on the dataset. The GRU consists of two layers of 128 units with a dropout of 0.5. The output is a softmax layer. Pre-trained word vectors are used as embedding layer.
38	The network works very well on the training and test dataset but is suboptimal on self-made data. There are various possible ways to solve that which must be studied.	38	The network works very well on the training and test dataset but is suboptimal on self-made data. There are various possible ways to solve that which must be studied.
39		39
40	Accuracy curves on the validation set are shown in the next image	40	Accuracy curves on the validation set are shown in the next image
41		41
42	![Accuracy](https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/accuracy.png)	42	![Accuracy](https://github.com/jgpavez/MedicalDiagnosis/blob/master/plots/accuracy.png?raw=true)
43		43
44	An example of output for data from the dataset is shown next. It is interesting to notice that the network outputs related diseases as the most probable 5, meaning that is understanding that those diseases are related (in this case all are psychological disorders).	44	An example of output for data from the dataset is shown next. It is interesting to notice that the network outputs related diseases as the most probable 5, meaning that is understanding that those diseases are related (in this case all are psychological disorders).
45		45
46	```	46	```
47	Predictions for data:	47	Predictions for data:
48	[[u'is', u'easily', u'distract', u'.'], [u'is', u'oftentimes', u'short', u'in', u'daily', u'activities', u'.'], [u'hyperactivity', u'symptoms', u'.'], [u'fidgetinesses', u'with', u'custodies', u'or', u'pess', u'or', u'wriggles', u'in', u'place', u'.'], [u'leaves', u'place', u'when', u'remain', u'sit', u'is', u'expect', u'.'], [u'runs', u'about', u'or', u'rises', u'in', u'inappropriate', u'situations', u'.'], [u'has', u'jobs', u'playing', u'or', u'workings', u'softly', u'.'], [u'is', u'oft', u'on', u'the', u'turn', u'acts', u'as', u'if', u'drive', u'by', u'a', u'motor', u'.'], [u'negotiations', u'excessively', u'.'], [u'impulsivity', u'symptoms', u'.'], [u'blurts', u'out', u'replies', u'before', u'enquiries', u'have', u'been', u'complete', u'.'], [u'has', u'trouble', u'look', u'crook', u'.'], [u'interrupts', u'or', u'irrupts', u'on', u'others', u'butts', u'into', u'conversations', u'or', u'games', u'.']]	48	[[u'is', u'easily', u'distract', u'.'], [u'is', u'oftentimes', u'short', u'in', u'daily', u'activities', u'.'], [u'hyperactivity', u'symptoms', u'.'], [u'fidgetinesses', u'with', u'custodies', u'or', u'pess', u'or', u'wriggles', u'in', u'place', u'.'], [u'leaves', u'place', u'when', u'remain', u'sit', u'is', u'expect', u'.'], [u'runs', u'about', u'or', u'rises', u'in', u'inappropriate', u'situations', u'.'], [u'has', u'jobs', u'playing', u'or', u'workings', u'softly', u'.'], [u'is', u'oft', u'on', u'the', u'turn', u'acts', u'as', u'if', u'drive', u'by', u'a', u'motor', u'.'], [u'negotiations', u'excessively', u'.'], [u'impulsivity', u'symptoms', u'.'], [u'blurts', u'out', u'replies', u'before', u'enquiries', u'have', u'been', u'complete', u'.'], [u'has', u'trouble', u'look', u'crook', u'.'], [u'interrupts', u'or', u'irrupts', u'on', u'others', u'butts', u'into', u'conversations', u'or', u'games', u'.']]
49	Disease: attention deficit hyperactivity disorder	49	Disease: attention deficit hyperactivity disorder
50	5 most prob. diseases: [u'attention deficit hyperactivity disorder', u'oppositional defiant disorder', u'seasonal affective disorder', u'anorexia nervosa', u'language disorder children']	50	5 most prob. diseases: [u'attention deficit hyperactivity disorder', u'oppositional defiant disorder', u'seasonal affective disorder', u'anorexia nervosa', u'language disorder children']
51	```	51	```
52		52
53	Next, we test the neural network on self-made symptoms, some examples are shown next. While in the first case the network correctly identify the disease, in the second case does not.	53	Next, we test the neural network on self-made symptoms, some examples are shown next. While in the first case the network correctly identify the disease, in the second case does not.
54		54
55	```	55	```
56	[u'oily skin', u'painful touch skin', u'face affected almost everywhere', u'chest affected', u'some blackheads', u'a lot of papules', u'papules', u'nodules', u'cysts']	56	[u'oily skin', u'painful touch skin', u'face affected almost everywhere', u'chest affected', u'some blackheads', u'a lot of papules', u'papules', u'nodules', u'cysts']
57	Disease: acne	57	Disease: acne
58	5 most prob. diseases: [u'acne', u'genital warts', u'scarlet fever', u'erythema multiforme', u'measles']	58	5 most prob. diseases: [u'acne', u'genital warts', u'scarlet fever', u'erythema multiforme', u'measles']
59		59
60	[u'pulsating feeling in stomach', u'persistent back pain', u'abdominal pain', u'severe pain in the middle abdomen', u'dizziness', u'clammy skin', u'tachycardia', u'loss of consciousness']	60	[u'pulsating feeling in stomach', u'persistent back pain', u'abdominal pain', u'severe pain in the middle abdomen', u'dizziness', u'clammy skin', u'tachycardia', u'loss of consciousness']
61	Disease: abdominal aortic aneurysm	61	Disease: abdominal aortic aneurysm
62	5 most prob. diseases: [u'pulmonary actinomycosis', u'mucormycosis', u'sleeping sickness', u'acute myeloid leukemia', u'bile duct obstruction']	62	5 most prob. diseases: [u'pulmonary actinomycosis', u'mucormycosis', u'sleeping sickness', u'acute myeloid leukemia', u'bile duct obstruction']
63	```	63	```
64		64
65		65